How does contextual embedding improve the interpretability of Generative AI-generated summaries

Question

With the help of proper code explanation can you tell me How does contextual embedding improve the interpretability of Generative AI-generated summaries?

Answer 1

Contextual embeddings improve the interpretability of Generative AI-generated summaries by providing richer, context-aware representations of input text. Here is the code snippet you can refer to:

• Context-Aware Representation: Embeddings reflect the meaning of words in context, leading to more accurate summaries.
• Improved Coherence: The model can generate summaries that better maintain logical flow and relevance.
• Transparency: Contextual embeddings can be visualized to understand better how the model interprets the input data.

Here is the code snippet you can refer to:

In the above code, we are using the following:

• Contextual Embeddings: Capture the meaning of words based on their context, improving summary quality.
• Summarization Accuracy: Leads to more coherent and contextually accurate summaries.
• Interpretability: Embeddings can be visualized or analyzed to explain the model’s decision-making process.

Hence, by leveraging contextual embeddings, Generative AI models produce more interpretable and meaningful summaries, with clearer reasoning behind the generated content.